Systems and Methods for Spin Process Video Analysis During Substrate Processing

ABSTRACT

In a liquid dispense system, camera images may be utilized to identify puddle edges of a liquid dispensed on a substrate. The camera image may be used to determine the percentage of puddle coverage and puddling non-idealities. The camera within a fluid dispense system may also be utilized to monitor the intensity of wavelengths reflected from a substrate during a spin coating step. The reflected intensity as a function of time as a substrate is spin coated may be used to monitor and characterize a spin coating process. The reflected intensity as a function of time may be compared to other substrates to identify substrate to substrate film thickness variations. The analysis may be based upon peaks and/or troughs of the reflected intensity as a function of time.

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/930,087, entitled, “Systems and Methods for Spin Process VideoAnalysis During Substrate Processing,” filed Nov. 4, 2019; thedisclosure of which is expressly incorporated herein, in its entirety,by reference. This application also claims priority to U.S. ProvisionalPatent Application No. 62/935,162, entitled, “Systems and Methods forAutomated Video Analysis Detection Techniques for Substrate Process,”filed Nov. 14, 2019; the disclosure of which is expressly incorporatedherein, in its entirety, by reference. This application also claimspriority to U.S. Provisional Patent Application No. 62/957,481,entitled, “Hardware Improvements and Methods for the Analysis ofSpinning Reflective Substrates,” filed Jan. 6, 2020; the disclosure ofwhich is expressly incorporated herein, in its entirety, by reference.

BACKGROUND

The present disclosure relates to the processing of substrates. Inparticular, it provides a novel system and method for monitoring one ormore characteristics of a substrate processing step. In one embodiment,the system and method disclosed herein may be utilized when processingsemiconductor substrates.

Traditional substrate processing systems utilize photolithographyprocesses, which include photoresist coating, exposure, photoresistdevelop, and various bake steps. The materials and processes utilized inthese steps may all impact film thickness, critical dimension targeting,line roughness, uniformity, etc. on a substrate. As geometries insubstrate processing continue to shrink, the technical challenges toforming structures on substrates increase.

In conventional substrate processing systems, a wafer inspection system(WIS) is often used to inspect a substrate (e.g., a semiconductor wafer)before or after one or more processing steps are performed. For example,a conventional WIS may determine a film thickness (FT) of a layer orfilm applied to a surface of a wafer after the wafer is subject to aPost Apply Bake (PAB) procedure to cure or harden the layer or film. Inanother example, a conventional WIS may determine a critical dimension(CD) of a structure formed on the wafer after the wafer is developed toform the structure. Such data may be provided to an advanced processcontrol (APC) system. APC systems may use statistical and/or analyticaltechniques to process the FT or CD value(s) received from a waferinspection system and determine how to manipulate process controlparameters and/or inputs on process tools to improve output quality. Forexample, an APC may use an average FT value determined from a WIS tocontrol the speed of a spin chuck disposed within a liquid processingsystem (e.g., a coating unit or developing unit) of a substrateprocessing system. In another example, an APC may use an average CDvalue determined from a WIS to control the temperature in a baking unit(e.g., a Post Exposure Bake (PEB) unit) of a substrate processingsystem. In addition, most wafer inspection systems are currentlyprovided as a separate module within, or coupled to, the substrateprocessing system. This adds complexity to the substrate processingsystem and forces the APC system to be a feedback system.

Gross processing equipment excursions or faults such as equipmentbreakdowns, material drips, improper arm movements, etc. are also knownto be monitored. One approach for monitoring gross processing issues incoating modules has been the inclusion of a camera in a coating moduleof a processing system. For example, coating modules have included spinmodule monitor (SMM) cameras which can be used to identify drips of thematerial being coated, improper dispense arm movements, etc. Images fromthe SMM camera may be analyzed after processing to determine if asubstrate was subjected to such process excursions or faults.

SUMMARY

Various embodiments of systems and methods for monitoring one or morecharacteristics of a substrate are disclosed herein. More specifically,the present disclosure provides various embodiments of utilizing cameraimages to provide information regarding characteristics of in a fluiddispense system.

In one embodiment, the location of the substrate within the fluiddispense system may be determined through the use of a camera in thefluid dispense system. More specifically, one or more edges of thesubstrate may be located. In one embodiment, the edge information may becombined with other information to determine if the substrate isproperly placed within the fluid dispense system. For example, dataregarding the substrate centering within the system may be obtained bydetermining the relationship between substrate edges and fixed objectswithin the system. In one embodiment, substrate centering informationmay be extracted through a comparison of the substrate edges to a cup ofthe fluid dispense system.

In another embodiment, characteristics of a puddle formed within thefluid dispense system may be obtained through analysis of the cameraimage. In one embodiment, edges of a liquid puddle formed on thesubstrate may be determined from the camera image. The puddle edgeinformation may be utilized in a variety of analysis techniques. In onetechnique, the percentage of puddle coverage of the substrate may beobtained. In one embodiment, the percentage of coverage of a reducingresist consumption (RRC) solvent may be obtained. In another technique,the puddle edges may be analyzed to identify non-idealities in thepuddle shape.

In another embodiment, the camera image may be utilized to analyze thelocation of a cup within the fluid dispense system. Because the camerais fixed within the fluid dispense system, variations in the location ofthe cup may be obtained from the camera image. Identification of thelocation of the cup may be performed to detect deviations in the cuplocation. Such deviations may occur, for example, after a cup isreplaced.

In one embodiment of the use of a camera within a fluid dispense system,the camera is utilized to monitor intensity of light reflected from asubstrate during a spin coating step. The reflected intensity as afunction of time as a substrate is spin coated may be used to monitorand characterize a spin coating process. The reflected intensity as afunction of time may be compared to other substrates to identifysubstrate to substrate film thickness variations. In one embodiment, theanalysis is based upon peaks and/or troughs of the reflected intensityas a function of time.

In another embodiment of the use of a camera within a fluid dispensesystem, reflected light intensity is obtained as a function of time as asubstrate is spin coated and signal processing techniques are performedto account for movement within the system. In one embodiment, the signalprocessing helps minimize the effects on light reflections caused by themovement of the pattern on the substrate that underlies the spin coatedmaterial. The signal processing techniques utilized may include datasmoothing, analyzing only certain wavelengths of reflected energy,transforming the data (in one embodiment utilizing a Fourier transform),and/or analyzing a sub-set of the collected pixels of data.

The camera image data collected herein may be combined with a widevariety of other data so as to better monitor, characterize and/orcontrol a substrate processing process flow. In one example, the cameraimage data may be combined with data collected from a WIS. In anotherembodiment, the camera image data may be combined with other datacollected from the fluid dispense system. Still further, the image datamay be combined with other data such as data related to the source ofthe liquid being dispensed (which liquid source bottle, the liquidsource bottle age, etc.).

According to one embodiment, a method of monitoring one or morecharacteristics of a fluid dispense system is provided. The methodcomprises providing a substrate within the fluid dispense system andobtaining a camera image of the substrate within the fluid dispensesystem. The method further comprises determining a location of at leastone edge of the substrate from the camera image. The method alsocomprises utilizing information regarding the location of at least oneedge of the substrate to analyze a placement of the substrate within thefluid dispense system.

In another embodiment, a method of monitoring one or morecharacteristics of a fluid dispense system is provided. The methodcomprises providing a substrate within the fluid dispense system andforming a liquid puddle on the substrate. The method further comprisesobtaining a camera image of the puddle formed on the substrate. Themethod also comprises identifying edges of the puddle from the cameraimage of the puddle.

In another embodiment, a method of monitoring one or morecharacteristics of a fluid dispense system is provided. The methodcomprises providing a cup within the fluid dispense system and obtaininga camera image of the cup within the fluid dispense system. The methodfurther comprises determining a location of at least one edge of the cupfrom the camera image. The method also comprises utilizing informationregarding the location of at least one edge of the cup to analyze aplacement of the cup within the fluid dispense system.

In another embodiment, a method of monitoring one or morecharacteristics of a fluid dispense system is provided. The methodcomprises providing a substrate within the fluid dispense system andspin coating a material upon the substrate. The method further comprisesutilizing a camera to obtain image data of the substrate over time whilethe material is being spin coated on the substrate. The method alsocomprises obtaining reflected intensity data over time from the imagedata. The method also comprises utilizing the reflected intensity dataover time to monitor and/or characterize the spin coating of thematerial upon the substrate.

In another embodiment, a method of monitoring one or morecharacteristics of a fluid dispense system is provided. The methodcomprises providing a substrate within the fluid dispense system andspin coating a material upon the substrate. The method further comprisesutilizing a camera to obtain image data of the substrate over time whilethe material is being spin coated on the substrate. The method alsocomprises obtaining reflected intensity data over time from the imagedata. The method also comprises utilizing signal processing techniqueson the reflected intensity data to account for movement within the fluiddispense system.

In another embodiment, a method of monitoring, characterizing orcontrolling a substrate process flow is provided. The method comprisesproviding a substrate within a fluid dispense system. The method alsocomprises obtaining a camera image of the substrate within the fluiddispense system, the camera image being a still image or a video image.The method further comprises collecting image data from the cameraimage. The method also comprises combining the image data with otherdata related to the substrate process flow so as to monitor,characterize or control the substrate process flow.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present inventions and advantagesthereof may be acquired by referring to the following description takenin conjunction with the accompanying drawings, in which like referencenumbers indicate like features. It is to be noted, however, that theaccompanying drawings illustrate only exemplary embodiments of thedisclosed concepts and are therefore not to be considered limiting ofthe scope, for the disclosed concepts may admit to other equallyeffective embodiments.

FIG. 1 is an exemplary fluid dispense system.

FIG. 2 illustrates exemplary camera locations for the fluid dispensesystem of FIG. 1.

FIG. 3A illustrates exemplary light locations for the fluid dispensesystem of FIG. 1.

FIG. 3B illustrates a plot of reflectivity versus resist thickness forwavelength assumptions.

FIG. 3C illustrates a physical placement relationship of a light source,camera and substrate.

FIG. 3D illustrates alternative placements of a camera and light source.

FIG. 3E illustrates plots of a reflectivity intensity versus frames fordiffering selections of pixels.

FIGS. 4A and 4B illustrate the relationship between a substrate edge anda cup edge.

FIG. 5 illustrate a puddle formed upon a substrate.

FIG. 6 illustrates the coverage percent versus time for a fluiddispensed on a substrate.

FIG. 7 illustrates a plot of an exemplary signal of reflected light as asubstrate is coated versus time.

FIG. 8 illustrates plots of exemplary signals of reflected light as asubstrate is coated versus time for seven different substrates.

FIG. 9 illustrates an expanded view of a portion of the plots of FIG. 8.

FIG. 10 illustrates an exemplary control loop for using a camera in afluid dispense system.

FIG. 11 illustrates exemplary smoothing of signal of reflected light asa substrate is coated versus time.

FIGS. 12A and 12B illustrate exemplary effects of removing awavelength(s) from the exemplary signal of reflected light as asubstrate is coated versus time.

FIG. 13 illustrates the effect of a Fourier transform of signal ofreflected light as a substrate is coated versus time.

FIG. 14 illustrates an exemplary camera image analysis process.

FIG. 15 illustrates an exemplary combination of camera image data withother process data.

FIGS. 16-21 illustrate exemplary methods of utilizing the techniquesdescribed herein.

DETAILED DESCRIPTION

The techniques described herein may be utilized within a wide variety offluid dispense systems. For example, an exemplary fluid dispense systemmay be utilized for various fluid dispense purposes (such as, forexample, a resist coating unit, a resist developing unit, or other spincoating units) within which fluid are applied to a substrate forprocessing purposes. It is recognized that the fluid dispense systemsshown herein are merely exemplary embodiments of a processing systemwithin which the monitoring techniques described herein may be applied.Thus, the techniques disclosed herein may be applied to other fluiddispense systems and/or other processing units. Moreover, these fluiddispense systems may be stand-alone units or more be integrated in alarger systems. For example, the fluid dispense systems described hereinmay be integrated within larger systems that include coating,developing, baking, inspection, exposure, etc. modules.

The fluid dispense systems described herein may be utilized to subjectsubstrates to a wide variety of processing liquids, which may be partof, for example, resist coating unit, a developing unit or other fluiddispense systems (such as for example, spin-on hard mask units, spin-onanti-reflective coating units, etc.). As shown in FIG. 1, a fluiddispense system 60 includes a processing chamber, which is bounded by achamber walls 62. A spin chuck 64 disposed inside chamber walls 62provides support for a substrate, which may in some embodiments, be asemiconductor wafer (W). More specifically, the spin chuck 64 has ahorizontal upper surface on which the substrate is supported duringprocessing. A suction port (not shown) may be provided in the horizontalupper surface of spin chuck 64 for securing the substrate to the spinchuck with suction. The spin chuck 64, and the substrate supported bythe spin chuck 64, may be rotated at a variable angular velocity by adrive mechanism 66, which may be a stepper motor, etc. The drivemechanism 66 may operate at various angular velocities for theapplication of the liquid material and flow of the liquid material ontothe substrate.

A nozzle 68 is adapted to dispense one or more liquid solutions onto thesubstrate at a specified rate to apply one or more layers or films ontoan upper surface of the substrate. Typical layers or films that may beapplied to the substrate surface include, but are not limited to,imaging layers (e.g., photoresist), develop solutions, topcoat (TC)barrier layers, topcoat antireflective (TARC) layers, bottomantireflective (BARC) layers, sacrificial and barrier layers (hard mask)for etch stopping, etc. The nozzle 68 is coupled to a liquid supply unit(not shown) through a liquid supply line 70. In some embodiments, nozzle68 may be attached to the leading end of a nozzle scan arm 72 through anozzle holder 74. The nozzle scan arm 72 is mounted at the upper endportion of a vertical support member 76 that is horizontally movable ona guide rail 78 in one direction (e.g., in the Y-direction). Althoughnot shown in FIG. 5A, a drive mechanism (not shown) may be coupled tothe nozzle scan arm 72, the vertical support member 76 or the guide rail78 to move the nozzle 68 in the Y-direction. Other mechanisms (also notshown) can be used to move the nozzle 68 in the Z-direction and/or inthe X-direction. It will be recognized that the particular dispense andarm mechanisms and movements described herein are merely exemplary as awide variety of dispense techniques are well known in the art.

A cup 71 is provided to capture and collect a majority of the liquidmaterial ejected from the substrate by centrifugal forces generatedduring rotation by the spin chuck 64. The spin chuck 64 supports androtates (i.e., spins) the substrate about its central normal axisrelative to the cup 71, which is stationary. Liquid material ejectedfrom the substrate 59 and collected by the cup 71 is drained via a drainline 65 and drain unit (not shown). In some embodiments, an exhaust line67 and exhaust unit (no shown), such as a vacuum pump or other negativepressure-generating device, may also be used to removes gaseous species(including but not limited to vapors released from substrate layersduring processing) from the processing space inside the cup 71.

Spin chuck 64 and drive mechanism 66 are disposed within an opening inthe cup 71. In some embodiments, an elevation mechanism, such as an aircylinder and an up-and-down guide unit, may be provided within drivemechanism 66 so the spin chuck 64 may move vertically relative to thechamber walls 62. The substrate can be delivered to the spin chuck 64 bya processing arm 61 through a loading/unloading opening 63 of fluiddispense system 60 in a direction 51 as shown in FIG. 1. The processingarm 61 may form a part of the fluid dispense system 60 or may be part ofa separate substrate transfer mechanism (not shown) for interacting withother process equipment. In some embodiments, the processing arm 61 maybe included within the main arm mechanism of a larger system fortransferring substrates to between various process modules of the largersystem. In other embodiments, the processing arm 61 may be includedwithin other substrate processing systems. In some embodiments, theelevation mechanism can lift the drive mechanism 66 and/or the spinchuck 64 upwards to receive a substrate. Alternatively, the cup 71 maybe configured to move up-and-down, or may be configured to separate andwiden, to allow a substrate to be placed on the spin chuck 64.

It is noted that the fluid dispense system 60 shown in FIG. 1 is merelyone example processing system in which the monitoring techniquesdescribed herein may be used. Thus, the fluid dispense system 60 is notmeant to be limiting, but rather merely representative of one exampleprocessing system within which the monitoring techniques describedherein may be utilized. Further, though the fluid dispense system 60 isdescribed with reference to a system for processing substrates, whichmay in some embodiments be semiconductor wafers, it will be recognizedthat the techniques described herein may be utilized when processingother types of substrates. Thus, it will be recognized that themonitoring techniques described herein may be utilized within a widerange of substrate processing systems that apply liquid solutions tosubstrates.

The fluid dispense system 60 also includes a light source 92 and acamera 90 as shown in FIG. 1. As used herein, “camera” may refer tosimply a camera or may be a more complex system that includes a cameraand other electronics. The camera 90 may be utilized to monitor thefluid dispense and coating process as described in more detail hereinbelow. The locations of the light source 92 and camera 90 shown in FIG.1 are merely exemplary and a wide variety of other positions may equallybe utilized to allow the camera 90 to monitor the condition of thesubstrate surface. FIGS. 2 and 3 provide a simplified top view(excluding many of the details of FIG. 1) of the fluid dispense system60 so as to better illustrate exemplary locations of the camera 90 andlight source 92. It will be recognized, however, that these locationsare merely exemplary and other locations may be utilized. As shown inFIGS. 2 and 3 the substrate 59 is provided within the chamber walls 62of the processing chamber which has a loading/unloading opening 63. FIG.2 illustrates exemplary locations for locating the camera 90 in theupper regions of the process chamber above the substrate. Morespecifically, FIG. 2 illustrates exemplary camera locations 201, 202,203, 204, 205, 206, and 207 for locating the camera 90. FIG. 3Aillustrates exemplary locations for locating the light source 92 in theupper regions of the process chamber above the substrate. Morespecifically, FIG. 3A illustrates exemplary light source locations 301,302, 303, 304, 305, 306 and 307 for locating the light source 92. Againit will be recognized that such locations of the camera and light sourceare merely exemplary and other locations may be utilized.

The analysis of a fluid dispense process with a camera may include awide range of techniques of analyzing and processing the images obtainedof the fluid dispense process. Such techniques may include analyzingstill images and/or analyzing video images obtained from the camera. Themonitoring of fluid dispense processes and the image obtained may beutilized for real time analysis/control and/or post process analysis.This image analysis may provide hardware and process feedback that mayotherwise not be available and can lead to improvements andoptimization. Image recording is an efficient method of data collectionthat can be done for every substrate. The image analysis can be used todetermine and/or control a variety of variables including filmthicknesses, critical dimensions, film uniformity, etc. In order toefficiently and accurately analyze images collected, automatedtechniques may be desirable.

The hardware utilized to monitor a spin coating process may be optimizedin a wider variety of manners so as to provide more accurate informationregarding the film formed on a substrate. More specifically, asdescribed below, a wide variety of hardware related techniques may beutilized, either in combination or singularly, to improve the collectionof data using the camera system. These hardware techniques may includeimprovements to the light source 92, improvements to the sensors of thecamera 90, the relationship of the physical orientation of the lightsource 92 to the camera 90, the selection of certain pixels of the imagefor analysis, and the relationship of the camera frame rate with therotational speed of the substrate.

Optimization of the hardware may address a variety of issues that arisewhen using an optical sensor during a spin coating process. In a spincoating process, it is possible to visually observe color changes on thewafer as the coated film decreases to its final coat thickness. Thesecolor changes are due to thin film interference reflectivity effects.For example when coating a resist layer on a substrate, these thin filminterference reflectivity effects are the effects of the resist/airreflected light wave with the resist/substrate reflected light wave. Asinterference of different wavelengths happen at different filmthicknesses, one may use the changing of the wavelength (and thus thecolor) that is having interference effect to monitor film thicknesschanges.

For example, FIG. 3B illustrates the impact on reflectivity as a filmthins during spinning. Note, in FIG. 3B the x axis represents resistthickness and the y axis illustrates the stack reflectivity. As shown inFIG. 3B, reflectivity changes for three different exemplary processeshaving three different wavelength assumptions are illustrated. Morespecifically, plot 405, plot 408 and plot 415 illustrate differentcenter wavelengths assumptions for a Gaussian distribution light sourceassumption in simulation. In simulation, the reflectivity at a givenfilm thickness assumption is determined.

Depending upon the film thicknesses involved and the underlyingsubstrate conditions and materials, however, detection, discernment,analysis, and correlation of the reflected signal may be difficult. Forexample, for a typical resist/substrate refractive index relationship,an optical path length thru the resist film that is a half multiple ofthe wavelength of the light divided by the refractive index of thematerial will have constructive interference with the same wavelength oflight that is reflected off of the air/material surface and will havedestructive interference with that same light if the optical path lengthis a quarter multiple of the wavelength of light divided by therefractive index of the material. As material decrease thickness, thewavelength(s) of light that will constructively interfere change andthus results in an oscillation of the visible spectrum color (orintensity) seen by an observing camera (or sensor). For some LED lightsources, that can have a significant spectral range, if means howeverthat there are situations in which a greatest common multiple thicknessrelationship happens between two distantly different wavelengths havingconstructive interference simultaneously which mixes the color responseseen by the camera and leads to loss of signal.

The physical location of the light source and camera are optimizedtogether. More specifically, as a reflectivity signal is beingcollected, the reflectivity signal strength may be maximized by ensuringthe 0^(th) order reflection of the light source off the substrate isbeing collected into the optical sensor (for example a camera). Doingthis also mitigates other effects, which are experienced in othersensor/light source orientation relationships such as light diffractioneffects from underlying highly reflective gratings. To ensure andmaximize the reflectivity signal, the physical locations of the lightsource and camera may be adjusted. Specifically, it may be desirableto 1) maintain a similar angle to a reference plane (that is parallel tothe substrate plane) of the light source and the optical sensor, 2)maintain a similar distance relationship of the light source to thecenter of substrate and the center of substrate to optical sensor and 3)to have the optical sensor positioned 180 degrees diagonally across fromlight source. Exemplary locations illustrating these concepts are shownin FIGS. 5 and 6. As shown in FIG. 3C, a light source 92 and camera 90are placed with reference to a substrate 59. The locations of the lightsource 92 and camera 90 may be chosen so that the angle of incidence 505and angle of incidence 510 of the figure are similar. In one embodiment,the angles of incidence are within 20 degrees of each other, in a morepreferred embodiment within 10 degrees, and in an even more preferredembodiment are approximately the same. Further, the distance from thelight source 92 to the center of the substrate 59 may be d, and thedistance from the camera 90 to the center of the substrate 59 may be thesame distance d as shown in FIG. 3C. In one embodiment, the distancesmay be within 10% of each other and in another embodiment 5% and in amore preferred embodiment substantially the same.

Further, as shown in FIG. 3D it may be desirable to have the lightsource 92 and camera 90 located across from each other. In oneembodiment, the light source 92 and camera 90 are substantially 180degrees diagonal from each other, and in another embodiment locatedwithin 10 degrees of being 180 degrees diagonal from each other and inanother embodiment within 20 degrees of being 180 degrees diagonal fromeach other. It is noted that the closer to being 180 degrees diagonalgenerally provides improved results. FIG. 3D illustrates two exemplarypairings of locations providing for such diagonal relationship. Forexample, the camera 90 may be placed at location 605A and the lightsource 92 may be placed at location 605B. This pairing of locationsprovides the desired 180 degree relationship. Likewise, alternativelocations 610A and 610B may be chosen for the light source 92 and camera90.

It will be recognized that the arrangements of FIGS. 3C and 3D aremerely exemplary locations to provide the angle and distance benefitsdescribed above and other locations may be chosen to achieve the sameangle and distance results.

A one embodiment, a specific selection of the pixels from which data iscollected in a frame of the substrate is performed. For example, allpixels that represent the substrate may be used for the benefit of pixelaveraging out any small differences in pixel absorption properties ofthe camera as well as sources of image noise (vibration, moving arms,slight changes of light source intensity, outside coat cup lightenvironment, etc.). However, inclusion of sources of image noise mightnot be desirable. Also, use of all pixels would include pixels thatrepresent non 0^(th) order reflections. However, use of only a subset ofpixels may address these issues and provide more accurate data. Forexample, if the light source/light spectral range is not well alignedwith camera absorption properties (e.g. the spectral tail of lightsource is the only thing that is being absorbed by camera) thenaveraging all of the pixels from a substrate leads to a loss of signal.One way to address this issue and to regain the signal is to limit thepixels selected to only those pixels in and/or in close proximity to theobservable primary reflection of the light source in the camera frame.Such pixels in and/or in close proximity to the observable primaryreflection of the light source in the camera frame represent the pixelsthat are most representing the 0 th order reflection of our lightsource. Similarly, selecting only a subset of pixels may allow for theexclusion of regional based noise sources. Thus, use of a selectedsubset of pixels may provide an improved signal from which dataregarding the conditions on the substrate may be extracted. The size ofarea that the subset of pixels may be limited to may be highly dependentupon the light source and camera combination utilized.

FIG. 3E illustrates exemplary effects of selecting only a subset ofpixels for analysis. As shown in FIG. 3E, plots of the average grayscale intensity (after removing the average frame from shortly afterdispense start to end of processing) for a series of frames that areobtained over time from the camera data (thus, the x-axis beingcollected over time). Plot 705 represents the intensity obtained overthe entire collected image from the camera. Plot 710 represents theintensity obtained when the data is limited to a region that correspondsto the substrate. It can be seen that for both of these plots, noisesources and the wide range of the reflections provide large areas ofnoise where the signal loses its cyclical nature over many frames. Plot715 and Plot 720 are plots in which only a subset of pixels of thesubstrate are analyzed. The size of area from which the subset of pixelsmay be limited to may be highly dependent upon the light source andcamera combination utilized. For example, when using an IR LED lightsource at 850 nm and a first camera with an integrated IR filter,extreme pixel masking to narrowly select the location of reflection onthe light source may be desirable, for example limiting the pixels to10% or even 5% or less of the pixels that correspond to the substratearea. However, in another embodiment utilizing different camera (CMOScamera) without an IR band pass filter and an IR LED source at 850nm,pixel masking may not be to the same level as the prior example toprovide a determination of the reflected signal (though masking maystill increase the amplitude of the detected signal). For example, pixelmasking of only approximately half the pixels that correspond to thesubstrate area may be utilized. As seen from the plots, the use of asubset of pixels provides a signal with better noise characteristics.

The techniques described herein are not limited to a particular cameraand light source type. The camera may be any of wide variety of types ofcameras designed to capture and/or store data from an image. The camerasmay collect still images and/or video images. A wide variety of camerasmay be utilized, including but not limited to, charged coupled device(CCD) image sensor cameras, complementary metal oxide semiconductor(CMOS) image sensor cameras, N-type metal-oxide-semiconductor (NMOS)image sensor cameras, indium gallium arsenide (InGaAs) image sensorcameras, indium antimony (InSb) image sensor cameras, etc. The lightsource may typically be a light source of the visible spectrum orlonger. For example, light sources in the visible spectrum,near-infrared (NIR), shortwave-infrared (SWIR) and mid-infrared (MIR)are exemplary light sources. In one embodiment, an amber light source inthe visible spectrum may be utilized. In another embodiment, an infrared(IR) light source is utilized. In yet other embodiments, amulti-spectrum light source may be utilized. It will be recognized thatmany cameras may include integrated filters that block the IR spectrum.The use of such filters may be undesirable if the IR spectrum is desiredfor analysis.

As mentioned above, monitoring of a wide range of variables andconditions of the fluid dispense process may be achieved through theutilization of a camera in the fluid dispense system. Various monitoringtechniques are described below. It will be recognized that thesetechniques need not be utilized together but rather may be utilizedindividually. Alternatively, some or all of the techniques may becombined for more thorough monitoring.

The use of a camera within a fluid dispense system may include usesrelated to substrate edge detection, puddle monitoring and cupmonitoring.

In one embodiment, the location of the substrate within the fluiddispense system may be determined through the use of a camera in thefluid dispense system. More specifically, one or more edges of thesubstrate may be located. In one embodiment, the edge information may becombined with other information to determine if the substrate isproperly placed within the fluid dispense system. For example, dataregarding the substrate centering within the system may be obtained bydetermining the relationship between substrate edges and fixed objectswithin the system. In one embodiment, substrate centering informationmay be extracted through a comparison of the substrate edges to a cup ofthe fluid dispense system.

In another embodiment, characteristics of a puddle formed within thefluid dispense system may be obtained through analysis of the cameraimage. In one embodiment, edges of a liquid puddle formed on thesubstrate may be determined from the camera image. The puddle edgeinformation may be utilized in a variety of analysis techniques. In onetechnique, the percentage of puddle coverage of the substrate may beobtained. In one embodiment, the percentage of coverage of a reducingresist consumption (RRC) solvent may be obtained. In another technique,the puddle edges may be analyzed to identify non-idealities in thepuddle shape.

In another embodiment, the camera image may be utilized to analyze thelocation of a cup within the fluid dispense system. Because the camerais fixed within the fluid dispense system, variations in the location ofthe cup may be obtained from the camera image. Identification of thelocation of the cup may be performed to detect deviations in the cuplocation. Such deviations may occur, for example, after cup replacement.

In a first embodiment of the substrate edge detection, puddle monitoringand cup monitoring techniques, monitoring via the camera may be utilizedto determine the edge of the substrate. One use of such monitoring is todetermine the centering of the substrate. For example, because thecamera and other elements of the fluid dispense system 60 (such as thecup 71) may be fixed objects, the relationship of the substrate to theother elements (for example the cup edge) may be used to extractcentering information indicative of proper placement of the substratewithin the fluid dispense system 60.

For example, if the substrate may be delivered by the processing arm 61above the spin chuck 64. A pin system within the spin chuck 64 may riseup to receive the substrate lower the substrate on the spin chuck 64. Inthe process of lowering the substrate to the spin chuck 64, the cameramay be utilized to determine the relationship of the substrate 59 to afixed point of the fluid dispense system 60, such as the front edge ofthe cup 71 and also the relationship of the substrate to the back edgeof the cup 71 to get information of substrate centering as eachsubstrate is delivered in real-time. Upon detection of deviations ofthis relationship, the system can issue an alert regarding a potentialsubstrate placement error and/or feedback of the placement error may beprovided to the processing arm 61 to actuate a correction.

FIGS. 4A and 4B illustrate the relationship that may be detected betweenedges 400 of substrate 59 and the cup 71. For example, FIG. 4Aillustrates an example image from a system having a camera placeddirectly above the substrate 59. As shown in FIG. 4A, by detecting theedges 400 of the substrate 59 utilizing the camera, the location of thesubstrate 59 may be determined relative to the cup 71. Moreparticularly, one or more distances between the edges 400 and the cup 71may be detected, such as for example distances 402, 403, and 404 shownin the picture. Detection of the relative locations of the edge and thecup 71 may be used to determine the centering of the substrate. FIG. 4Billustrates an example image that may be obtained from a system having acamera above, but to the side of, the substrate 59. Again, the image maybe utilized to detect the relative location of the edges 400 of thesubstrate 59 with regard to the cup 71 as shown by one or more exampledistances 410, 411, 412, and 413. Thus, once again centering informationof the substrate may be obtained.

In a second embodiment of the substrate edge detection, puddlemonitoring and cup monitoring techniques, information regarding a liquidpuddle formed on the substrate may be detected. More particularly, acommon technique utilized in spin coating processes is to form a puddleon the substrate of a liquid material (typically prior to spinning or atthe beginning of a low speed spin). In one embodiment, the liquidmaterial that is dispensed to form a puddle may be the coating material(for example photoresist). In another embodiment, the material which isformed via a puddle may be a solvent pre-wet material applied to thesubstrate prior to the coating material so as to change the surfaceenergy of the substrate to be coated. In one example the pre-wetmaterial may be used for the purpose of providing easier wettability forlower material consumption of the coating material. One well-knownexample of such pre-wet material is a reducing resist consumption (RRC)solvent.

By utilizing the camera video monitoring, useful information about thesurface energetic state of the incoming substrate may be obtained. Forexample, by detecting the edges of the substrate with the camera 90 andthe extent of the puddle flow of the puddled material (for example a RRCsolvent puddle), the percent substrate coverage by material may bedetected. For example, the number of pixels covered by an RRC solventpuddle may be compared to the total number of pixels associated with thesubstrate and a percent coverage may be calculated before a RRC solventspin cast step begins. By comparison of the percent substrate coverageto previous results for percent substrate coverage from same substrateprocessing step for other substrates (or comparison to a desiredbaseline result), detection of substrate surface condition changes canbe identified. Alternatively, data on the dynamic measurement of thecoverage state during RRC puddle formation may be collected over timeand again compared to prior results or expected baseline results. Use ofcomparisons to analyze the percent of wafer coverage of the puddle maythus be utilized to determine if a substrate surface condition issimilar to an expected result or different from what is expected. Suchinformation may be collected and used to characterize, control,evaluate, and/or monitor the processing in a substrate process flow.

In a third embodiment of the substrate edge detection, puddle monitoringand cup monitoring techniques, analyzing the formed puddle may be usedmore generally to evaluate the spin coat process and/or the state of thesubstrate to be coated. Thus, for example, any material within the spincoat process which would be affected by a change the surface energy ofthe substrate may be analyzed by considering the results of puddlemonitoring. Thus, for example, as discussed the puddle analysis mayreflect the wettability and associated material consumption. However,more general process fault detection may also be performed by utilizingthe camera monitoring to evaluate the puddle formation. Thus, a varietyof spin coat materials may be monitored during a puddle process. Suchmonitoring may be during a static dispense (not rotating duringdispense). In other embodiments, the monitoring may be during a lowerrevolutions per minute (RPM) condition of the dispense step. Collectedinformation as to the extent of the formed puddle provides usefulprocess information about the surface energetic state of the incomingsubstrate. By using an edge finding algorithm, the percent wafercoverage of the static dispense puddle formed (number of pixels coveredby material/total number of pixels associated with the wafer) before thematerial cast step can then be known. Use of comparisons to previousresults (or baseline results) for percent wafer coverage from samesubstrate process flow may be used to judge if substrate condition issimilar or has changed. Alternatively, a dynamic measurement of thecoverage state thru dispense and cast (depending on viscosity) may bedetected and used for process monitoring and/or to control variousvariables during processing. Further, the puddle formation percentagecoverage as a function of time may also be used for comparisons toprevious results from the same substrate process flow to determine ifthe substrate condition is similar to or has changed from what isexpected.

The second and third embodiments of the substrate edge detection, puddlemonitoring and cup monitoring techniques discussed above may provideinput parameters into advanced process controllers or process controlcomputer systems for potentially providing valuable information forfault detection. For example, a wide range of process faults may beflagged, including for example but not limited to identifying pooradhesion conditions, identifying degradation of adhesion material,identifying incoming substrate condition variations and identifying theextent/quality of surface preparation material (e.g. brush material indirected self-assembly (DSA) lithography applications.

In one analysis embodiment utilizing camera 90, the edge of the fluid asit is detected on the substrate may be detected. In one example, thedetected edge may be the original puddle formed by the dispense prior tospinning the substrate. In another embodiment, the edge may representthe puddle as it is spread during spinning. FIG. 5 illustrates anexample image of a puddle 500 formed upon a substrate 59 having edges400. It will be recognized that though FIG. 5 illustrates a center topcamera image, the image may be obtained from any of a wide variety ofcamera placements. It will also be understood that the puddle 500 may beformed of any of a wide variety of materials that are deposited upon asubstrate to form a puddle. The image obtained may be analyzed to detectintensity, color and/or greyscale difference or gradient differences inintensity, color or greyscale across the substrate so as to determinethe edges of the puddle 500 of material that is deposited on thesubstrate. The differences may be differences between the exposedsubstrate areas and areas of the substrate covered by the puddle. Thisinformation may be utilized so as to determine the edges of the puddle500 of material that is deposited on the substrate. The percentage ofcoverage of the puddle may be calculated from such image analysis. Theimage analysis may be performed upon one static image (for example afterdispense but immediately before spin). Alternatively, the image analysisof the puddle may occur dynamically over a period of time, for exampleby analyzing a video or multiple still images (including images obtainedduring spinning of the substrate). Thus, for example, as shown in FIG.6, the percent of coverage may be plotted against time as shown by curve600. In one embodiment, the puddle analysis data may be collected fromjust prior to dispense through the completion of dispense prior tospinning. In another embodiment, the puddle may be analyzed even duringspinning of the substrate. For analysis that occurs during spinning,higher resolution cameras may be needed because as the depositedmaterial thins due to spinning, the differences across the substratewill become more subtle and more difficult to detect.

The dispense edge detection provides a method to analyze a dispenserecipe and the associated fluid coverage through time. During a processin which fluid is applied to a substrate, the substrate may spin to movethe fluid outward to cover the substrate. The spinning of the substratemay occur before, during, or after the dispense of the fluid starts andmay change speed at which it spins throughout the recipe. As thesubstrate spins, the dispense edge detection techniques provide a way tomeasure how much of the substrate is covered at any point in time.Detection of the dispense edge may also be used to provide feedback tothe fluid dispense system and help determine how effective the dispenseprocess is.

The dispense edge detection technique may use a variety of detection anddata processing methods, including some that are used to detect thedispense start frame. In one embodiment, first the dispense recipe isidentified and the appropriate frames determined to be used foranalysis. For example, in order to find the start of the recipe the armmovement into the field of view, the movement downwards toward thesubstrate, and the end of the downward movement may be detected asdiscussed above. Once the end of the downward movement is detected, apredetermined set of pixels may be used to search for the initialdispense on the substrate based on the maximum intensity change.

From the start of the dispense on the substrate of the fluid, theinitial outline and outer edge of the fluid on the substrate may befound by analyzing the intensity change across the substrate over time.Once the outer edge of the dispense is found, a shape may be fit totrack the movement outwards throughout the recipe based on the cameraangle (for example a circular puddle of fluid on the substrate will nothave a circular image pattern if the camera is located in a corner ofthe fluid dispense system). Similar to a series of circles with varyingradii, multiple iterations of the previously determined shape may befitted moving outward from the initial dispense to the edge of thewafer. It may then be determined which pixels fall within a giveniteration of the shape but not within any of the other iterations. Thenfor each frame in the recipe after the dispense is detected, theintensity may be calculated for each set of pixels within each shapeiteration. In one embodiment, the intensity difference is calculated foreach frame and a possible threshold or filters may be used to ignorecertain intensity differences. For the set of pixels calculated from theintensity difference, it is then determined which shape iteration eachof these pixels fall within. This may then be used to detect the edge ofthe dispense for the current frame in the recipe. Once the edge isdetected for each frame in the recipe, the number of pixels within eachpuddle detected for each frame may be calculated. In addition, the ratioof the number of pixels within each edge and total number of pixelswithin the substrate may be calculated. This calculation gives insightinto the coverage of the substrate throughout the recipe and thecoverage rate of change. It will be recognized that other calculationsmay also be utilized to characterize the edge of the fluid on thesubstrate. The analysis may include an analysis of the whole image frameor only an analysis of a subset of pixels of the image frame (forexample pixels in which the dispense fluid is expected to be presentduring the actual dispense of fluid).

One exemplary embodiment of a workflow for tracking the puddle edge maythus be as follows. First, the appropriate frames to analyze from thecamera data are determined. This determination may be based upon thehardware movement detection and/or detection of the dispense start asdescribed above. Thus, the analysis may be focused on the relevantframes proximate in time to the formation of the puddle. Second, theouter edge of the initial dispense edge is determined based on theintensity difference for a given set of pixels. Third, a shape is fit tothe initial dispense puddle edge based on the camera angle. This shapeis then used to track the puddle edge throughout the recipe. Fourth,throughout the recipe, multiple iterations of the shape are fit to thecurrent frame moving outwards from the shape fit for the previous frameto the edge of the substrate. For example if a circle was fit to theinitial puddle, multiple circles with each having a slightly largerradius than the previous, would be fit to the current frame. Fifth, thedifference is calculated for each frame and the edge is found from themaximum number of points that fall within a given ring or area of themultiple iterations of the shape. In one embodiment, filtering of thedata may be applied before the intensity difference analysis so as tolimit the amount of data that needs processing. Without filtering, theamount of data present makes fitting the data more difficult.Furthermore, in one embodiment only fitting of the outer most datapoints of the puddle is performed to further enhance the accuracy andspeed of the calculations. In addition, filtering of data points andshapes may further be based on tracking fits for previous frames and therate of puddle expansion over time.

In a fourth embodiment of the substrate edge detection, puddlemonitoring and cup monitoring techniques, the camera 90 may be used forassessment of wettability issues of the develop solution on top of theresist which leads to significant macro defects. For example, developsolution pullback may be detected. In this analysis, camera monitoringmay be utilized to detect puddle edges of the develop solution. Forexample, edge finding algorithms may look for any non-ideality in thedevelop puddle. Such non-idealities may be, for example but not limitedto, random zones of pullback on substrate, puddle fingering at edges asdevelop puddle expands, etc. Upon determination of non-idealities in thepuddle formation, the system may cause an alert. In one embodiment, thealert may be used to indicate a potential substrate placement error. Insome embodiments, the alert of potential substrate placement errors maybe used to provide feedback of the placement error to a robotic arm forplacement correction.

In a fifth embodiment of the substrate edge detection, puddle monitoringand cup monitoring techniques, use is made of the fact that the camera90 is a fixed object within the video scene within the process chamberbounded by the chamber walls 62. With the understanding that the camera90 is fixed, the relationship of the cup 71 edge in an X and Y cut planemay be used to extract cup location information. Thus, by taking a cutplane in X (and/or in Y) of the image detected by the camera, the cuplocation may be identified from intensity, color and/or greyscaledifference or gradient differences in intensity, color or greyscale(i.e. identifying where the cup 71 is located within the image frame).Such cup location detection may be performed from a camera image eitherwith or without the substrate present. Preferably, the image may becaptured at a point in time in the process sequence when arm movementsare not affecting the cut plane signal. The cup location detected can beused for monitoring and the system may look for deviations of cuplocation. Alerts may then be provided within the system as to when adeviation from expected cup locations are detected. For example, aftercup replacement, deviations may be detected that result from eitherusage of the wrong cup or a cup placement that is off relative to anexpected baseline (for example the cup is not properly placed duringreplacement). In such cases, an alert may be generated. Also, if a cupshifts over time during usage of the system, an alert may be generated.

Coupled to (or even part of) the fluid dispense system 60 as shown ofFIG. 1 may be a controller 94 for setting and controller various processoperation parameters of the system. The controller 94 may be coupled tothe camera 90 and light source 92 as shown. The controller 94 may alsobe coupled as indicated by signal line 96 to any or all of a number ofthe components of the fluid dispense system 60 to receive informationfrom and/or to control the components. For example the controller 94 mayreceive information from and provide control information to theprocessing arm 61, spin chuck 64, drive mechanism 66, nozzle 68, nozzlescan arm 72, etc. The controller 94 may also be generally configured toanalyze various data collected by the fluid dispense system, and in somecases provide feedback control to various process operation parameters.Thus, the techniques for data processing and system control describedherein may be implemented by a controller 94. It is noted that thecontroller(s) 94 described herein can be implemented in a wide varietyof manners. In one example, the controller may be a computer. In anotherexample, controller 94 may include one or more programmable integratedcircuits that are programmed to provide the functionality describedherein. For example, one or more processors (e.g., microprocessor,microcontroller, central processing unit, etc.), programmable logicdevices (e.g., complex programmable logic device (CPLD)), fieldprogrammable gate array (FPGA), etc.), and/or other programmableintegrated circuits can be programmed with software or other programminginstructions to implement the functionality described herein forcontroller 94. It is further noted that the software or otherprogramming instructions can be stored in one or more non-transitorycomputer-readable mediums (e.g., memory storage devices, flash memory,dynamic random access memory (DRAM), reprogrammable storage devices,hard drives, floppy disks, DVDs, CD-ROMs, etc.), and the software orother programming instructions when executed by the programmableintegrated circuits cause the programmable integrated circuits toperform the processes, functions, and/or capabilities described herein.Other variations could also be implemented.

In the manner described above, intelligent control of the fluid dispensesystem may be provided through the use of data collected by a cameraplaced within the fluid dispense system. The collected data may be usedfor, but not limited to, flagging deviations in substrate centering,deviations of surface energetics of incoming substrates, andnon-idealities/excursions of puddling. The data collected from thecamera images is not limited to a particular form. For example, in oneembodiment, color data may be collected and detected differences ofcolor may be utilized to identify the various features mentioned above,such as for example, substrate edges, cup edges, puddles, etc. Inanother embodiment, greyscale information may be obtained and changesacross the image utilized to identify the features. Further, justsub-sets of information may be utilized. For example, even though redgreen blue (RGB) channel color data may be obtained, only one channelmay be analyzed for detection of the various features such as substrateedges, cup edges, puddles, etc. For example, the changes in the Rchannel alone may be utilized to identify substrate edges, cup edges,puddles, etc. Further, it will be recognized that a the data analysisand data processing techniques applied to the image data may beaccomplished in a wide variety of manners so as to achieve the resultsdiscussed herein, and the techniques described are not limited to aparticular approach.

Film Thickness Measurement of Spin Coated Films Using Light Reflection

In one embodiment of the use of a camera within a fluid dispense system,the camera is utilized to monitor the intensity of wavelengths reflectedfrom a substrate during a spin coating step. The reflected intensity asa function of time as a substrate is spin coated may be used to monitorand characterize a spin coating process. The reflected intensity as afunction of time may be compared to other substrates to identifysubstrate to substrate film thickness variations. In one embodiment, theanalysis is based upon peaks and/or troughs of the reflected intensityas a function of time.

Thus, the fluid dispense system 60 of FIGS. 1-3 may also be controlledto provide a film thickness monitoring technique during spin coating. Anexample film spin coating technique involves a polymer dissolved in asolvent. As coating material is dried to evaporate the solvent, a filmof high uniformity is left behind. Typically, film thicknesses for spincoated films are measured after coat and/or after a post apply bake(PAB) using a stand-alone spectroscopic tool. The fluid dispense system60 and techniques disclosed herein allow for real-time monitoring of thefilm thickness as a film is coated. In addition, such techniques aremore conducive to various process control schemes.

As the spin coated material dries, visible color changes may be observedas the resultant film thickness dynamically changes and finallystabilizes. The fluid dispense system 60 and associated camera 90 may beused monitor film thickness uniformity by detecting color changes duringthe spin coating process. More specifically, video images obtained bycamera 90 during the coat process may be used to monitor substrate tosubstrate film thickness uniformity. Specifically, the reflected lightfrom the substrate changes during the spin coating process due to thecoated film drying. This change may be used to determine if the filmthickness was consistent to the other substrates (or baselinesubstrates). Thus, the dynamic color change that a drying film exhibitsmay be an identifying fingerprint for the final film thickness, and so,process control schemes may be built around this ability to monitor thecoated film thickness in real time.

Shown in FIG. 7 is an exemplary plot of signal 700 of all reflectedlight detected by a camera 90 as a substrate is spin coated over time.As seen by signal 700, as time progresses, peaks and troughs in theintensity value of the reflected light occurs as the film dries and thereflectivity changes. A wide range of characteristics of the reflectionsignal may be utilized to characterize and monitor the coating process,including but not limited to the amplitude of the various peak to troughreadings, the placement of the peaks and troughs, and the way in whichthe sinusoidal period changes throughout the process. Moreover, more indepth data processing of the signal data may be performed, including butnot limited to various kinds of transforms to the signal (Fourier,Laplace, etc.) and looking for characteristics in those data sets thatmight allow for a more clear analysis of a characteristic nature to agiven film thickness and/or coating process. Further, it is noted thatin the example signal 700 of FIG. 7, the data collected is for allwavelengths of visible light. However, the data may be broken down intoRGB components or even analyzed at a single wavelength or some sub-setof wavelengths.

In one embodiment, the spin coating process may be characterized andmonitored to establish substrate to substrate film thickness variationsby tracking the placement of the peaks and/or troughs along a time axis.FIG. 8 illustrates the reflection data of seven different substratesduring a spin coating process. In the example of FIG. 8, five substratesare processed with the same standard process. Typical standard processesutilize a casting RPM of 1200 to 1800 RPMs. The reflectivity lightintensity over time is shown in plots 802, 804, 806, 808, and 810respectively. Two substrates are spin coated at different spin speeds.One substrate is coated at an increased RPM of +50 RPM (as compared tothe standard process) and is indicated by plot 814. Another substrate iscoated at a decreased RPM of −50 RPM and is indicated by plot 812.

In the example of FIG. 8, the signal from an image camera is convertedfrom RGB to greyscale intensity. The greyscale intensity may then beaveraged over the substrate area and plotted through time. It is notedthat the intensity (vertical axis) may be significantly differentbetween the substrate runs, even if the same process is utilized. Thismay result from the use of different fluid dispense systems and thelight level inside the cup not being constant, among other factors.However, it is noted that the horizontal placement of the peaks andtroughs are very similar for all five cases in plots 802, 804, 806, 808,and 810. The noise in the time at which each peak or valley occurs is onthe order of the frame rate. For the cases where the rotation speedduring drying was altered by 50 rpm (plot 812 and plot 814) the peaksand valleys have shifted indicating that a change in the final thicknesswill be present. The ratio of this signal to noise is used to judge thesmallest thickness change capable of being detected.

It is noted that the decreased and increased RPM processes provideapproximately ±1.5% film thickness differences. In the example shown, a0.5 nm mean thickness change may result by changing the RPMs by 50 RPMs.The time sensitivity corresponds to roughly 0.15 seconds time shiftequaling approximately 1 nm film thickness change. Depending upon thenoise in the signal differing amounts of thickness changes may bedetected by monitoring the time shift. In one embodiment, thicknesseschanges of greater than 0.3 nm may be detected by monitoring the timeshift in the signal.

As indicated by the plots 802, 804, 806, 808 and 810, the results fromsubstrates coated according to the same process indicate that arepeatable signal may be obtained substrate to substrate. As shown inFIG. 8, the signals obtained from substrates processed at different RPMsprovide displacement from the standard process on the time axis. Morespecifically, the displacement of the peaks and troughs between plot 812and plot 814 is indicated by arrows 820, 822, 824, 826, 828, and 830 inFIG. 8. This displacement can be used to detect the difference in filmthickness. FIG. 9 illustrates an expanded view of the time between 30.6and 31.5 s on the graph of FIG. 10, illustrating the displacement 828.As also can be seen from FIG. 9, though the value of the peaks of plots802, 804, 806, 808 and 810 may vary, the time occurrence of the peaks isvery repeatable for the same standard baseline condition. Moreover, aclear resolution of the peaks for the films cast at different spinspeeds (and thus associated film thickness variations) may be obtained.Thus, even though the peak value of the intensity from substrate tosubstrate may show significant variation and noise, the time placementof the peak may be utilized to characterize and monitor a particularspin coating process. In this manner, reflected intensity data over timeis obtained for a plurality of substrates and the monitoring and/orcharacterizing includes comparison of reflected intensity data of aplurality substrates.

In this manner, the detected reflection data obtained by a camera in aspin coating chamber may be utilized to monitor and characterize thespin coating process in real-time. Further, such data may be provided toa controller such as controller 94 for collection, recordation,analysis, process control, etc. Moreover, the data analysis may beperformed in a wide range of manners and is not limited to thatdiscussed in relation to FIGS. 7-9. For example, in the discussion abovethe time resolution of one single peak was described. However, a moredetailed analysis can identify multiple (or even all) of the peaks andthe troughs in the signals and use some (or all) of them in a similarmanner in order to provide more data for the metric. In this way, onemay employ a series of time steps for the peaks and troughs as well asthe time increments between these peaks and troughs to create afingerprint for a given film coating on a given substrate. Further, dataanalysis could be accomplished by removing one of the RGB channels fromthe analysis or even specific wavelengths may be utilized to accomplishthe analysis. Thus, light source 92 may be configured with specificdesired wavelength(s), and the best one could be used for a givenfilm/substrate configuration. Alternatively, filtering of the collectedreflectivity wavelength data may be accomplished in the camera or evenperformed by software in the controller. Thus, the system could beconfigured to expose and capture a spectrum of wavelengths which areanalyzed. Complex signal processing on the recorded signal may also beperformed by the controller in order to remove certain noise sources andimprove the signal to noise ratio.

The data collected according to the techniques described above may beutilized to monitor and/or characterize a process, particularly foridentifying variations from substrate to substrate or detecting theoccurrences of faults or excursions in a coating process. However, asthe data may be collected in real-time during the spin coating process,the data may also further be utilized to provide real-time control ofthe spin coating process. More specifically, a well-known relationshipof spin speed to film thickness is that thickness is proportional to oneover the square root of the spin speed. Therefore, since real-time filmthickness difference data may be obtained, a control loop may beprovided whereby the spin speed may be corrected during spin processingto obtain a correct film thickness. FIG. 10 illustrates an exemplarycontrol loop. As shown in FIG. 10, a feedback control process 1000 maybe provided. As shown in FIG. 10, a spin motor control block 1002impacts the coating obtained in a coat cup control block 1004 which ismonitored by a camera control block 1006. Data from the camera controlblock 1006 is feedback to a controller block 1008 which may providefeedback control to the spin motor control block 1002 so as to adjustthe spin speed in real-time so as to adjust the final thickness of thefilm. In this manner real-time monitoring of the reflectivity dataobtained by a camera 90 may be utilized to provide real-time filmthickness control during the application of a film in a fluid dispensesystem 60 shown in FIG. 1.

Data Analysis Techniques for Spinning Patterned Reflective Substrates

In another embodiment of the use of a camera within a fluid dispensesystem, reflected light intensity is obtained as a function of time as asubstrate is spin coated and signal processing techniques are performedto account for movement within the system. In one embodiment, the signalprocessing helps minimize the effects on light reflections caused by themovement of the pattern on the substrate that underlies the spin coatedmaterial. The signal processing techniques utilized may include datasmoothing, analyzing only certain wavelengths of reflected energy,transforming the data (in one embodiment utilizing a Fourier transform),and/or analyzing a sub-set of the collected pixels of data.

More specifically, as described above, reflectivity data may becollected within a fluid dispense system 60 utilizing a camera 90.However, the analysis of the collected data may be complicated bymovement of various components within the fluid dispense system and alsoby the existence of patterns on the substrate being coated. In oneembodiment, data analysis techniques may be utilized to remove orminimize the impact of moving parts above the substrate and/or changesin reflections caused by the underlying patterns that exist on thesubstrate being coated which vary in orientation as the substrate spins.In one embodiment, the image frame obtained when the coating materialfirst exits the dispense nozzle is identified. Starting at that point, aplot of reflection intensity through time may be generated for any pixelof the video image that contains the substrate. The data may then besmoothed and/or transformed to minimize higher frequency noise due toreflections from the varying orientations of circuit patterns on thesubstrate. Analysis may be used to determine which wavelengths of lightand which pixels should be included to achieve the best signal to noise.The peaks and valleys in the reflection curves may then recorded andcompared with the previous substrates to determine any shift inthickness as described above and/or used to control the fluid dispensesystem in real-time as also described above.

In order to compare the timing of the peaks and troughs that aredescribed above, it is desirable to ensure that the data in theintensity vs time chart is collected starting at the same moment atevery substrate. This may be done in a wide range of manners. In oneapproach, edge detection threshold are set to observe differencesbetween an image frame before dispense and an image frame afterdispense. First the movement of the dispense arm to the target positionmay be signaled by having a certain number of pixels detecting the armin certain target areas. Then, the dispense is detected by looking atthe difference between frames and counting a specified number of pixelsin a target area where the dispensed material is to be located.

In the case of substrates with reflective patterns on the substrate, itcan be much more difficult to discern a signal. The variation inintensity from the changing orientation of the substrate can exceed thecolor change from the changing thickness. To solve this problem,multiple techniques may be employed. In one embodiment, the data may befiltered to smooth the data until the separation of each peak and valleyfalls within an expected range. For example, as shown in FIG. 11, anoriginal signal 1102 may be subjected to a data smoothing algorithm. Twoexamples of a smoothed signal as shown by signals 1104 and 1106 whichare shown on the same time axis (though shifted on the intensity axismerely for ease of illustration). The resulting smoothed signals may bebetter suited for examination of the peaks and troughs as the filtereddata removes much of the noise in the system as shown.

In another embodiment, certain wavelengths may be removed from thesignal as part of the data processing so that the peaks and troughs maymore accurately be compared for establishing thickness changes. Forexample, in one embodiment, the red, blue, or green wavelengths signalsfrom the camera image can be individually turned off before convertingthe signal to greyscale. The desired signals to remove may varydepending on the diffraction of the underlying pattern. In oneembodiment, all seven permutations of the potential RGB signals may beevaluated to establish which permutation provides the highest signal tonoise ratio for a particular underlying pattern.

FIGS. 12A and 12B illustrate an exemplary effect of removing awavelength. FIG. 12A illustrates data using all wavelengths. FIG. 12Billustrates data having the green wavelength band (wavelengths 534-545nm) removed. As shown in FIG. 12A, three signals are obtained from acommon process, signals 1202A, 1204A, and 1206A.

One signal is obtained by adjusting the common process by increasing thespin speed by 50 RPMs. FIG. 12B shows the data for the three signalsfrom the common process with the green removed, signals 1202B, 1204B,and 1206B. Data from the process having the spin speed increased by 50RPMs is shown as signal 1208B. As shown in FIGS. 12A and 12B, removal ofthe green band improves the identification of the peaks and/or troughs.In the example, the noise is reduced for a 1 nm thickness change fromapproximately 0.2 seconds to 0.1 seconds.

In yet another embodiment, noise introduced by variability between theframe rate of the camera and the rotation speed of the substrate may beaddressed. More specifically, depending upon the rotation speed and theframe rate, each frame may be obtained for a different orientation ofthe patterned substrate. Such differences will create noise in thedetected signals. This mismatch generates a cyclical fluctuation inintensity with a relatively constant frequency. Thus, it is desirable toidentify and remove this added color change that results from themismatch. This noise may be addressed by utilizing a Fourier transformto remove substrate orientation effects as the cyclical fluctuation inintensity with a relatively constant frequency can be observed as a peakin the Fourier transform. By setting the undesired frequencies to zeroand performing the inverse Fourier transform the impact of the framerate mismatch can be removed. For example, for a frame rate of 30 framesper second (fps) and substrate rotation speeds of 1200 or 1800 RPMsmismatch will not occur. However, for rotation speeds of 1750 and 1775RPMs, mismatch may occur. FIG. 13 illustrates data from eightsubstrates, six collected at either 1200 or 1800 RPMs and one at 1750RPMs and one at 1775 RPMs. The Fourier transform of the intensity vstime curve is shown in FIG. 13. As shown in FIG. 13, the Fouriertransform of the curve readily illustrates the frequencies of theundesired reflections for the 1750 RPM signal, at points 1302, and the1775 RPM signal at points 1304.

Another noise reduction method examines the intensity through time imagedata pixel by pixel. Specifically, certain locations on the patternedsubstrate may provide higher noise then other locations. Analysis may beperformed to identify locations on the substrate which are less noisyand those locations may be utilized for the film thickness techniquesdescribed herein. Thus, though the image data may comprise a pluralityof pixels, the pixels of the image that correspond to the location whereless noise is observed may be utilized for the film thicknesstechniques. One method to establish which pixels to utilize for aspecific patterned substrate involves analyzing the substrate when thedeposited film is dry. For such a substrate, the intensity is expectedto be relatively stable. By monitoring the image data from a spinningdry substrate, the variation amongst pixels of the image may bedetermined. The pixels having high variation accordingly correspond tothe pixels that are more affected by undesired reflections from theunderlying pattern. Any pixel that exceeds a variation threshold, asdetermined by standard deviation through time or the presence ofundesired frequencies in a Fourier transform corresponding to systematicreflections, may be removed from the calculation of average intensity.In this way, particular pixels, or groups of pixels may be removed fromthe analysis to reduce the noise in the image data and the processproceeds by analyzing only image data from a sub-set of the plurality ofpixels of the camera.

As described above, a variety of data analysis techniques for analyzinga spinning patterned reflective substrates during a formation of a filmupon the substrate have been provided. Each of these techniques may beutilized individually to provide an improved analysis approach.Alternatively, various combinations of the techniques may be utilized.Thus, as described herein, the techniques may be utilized singularly orin combination. In one embodiment, an image analysis process for usewith a spin coating process may be utilized together a grouping of thesetechniques as described in FIG. 14. As shown in FIG. 14, a camera imageanalysis process 1400 is provided. First, in step 1402 the frame atwhich dispense states may be determined. Then, an optimization loopcomprised of step 1404, step 1406, step 1408 and step 1410 may beperformed (once or more often) until desired conditions are found atstep 1412. More specifically, at step 1412 conditions are found suchthat noise from movement in the system has been sufficiently removedsuch that peak or trough shifts may be identified that correspond tothickness shifts on the substrate. More specifically, step 1404comprises determining peaks and valleys in intensity corresponding tofilm thickness. Then, step 1406 comprises smoothing data to only containfrequencies of interest and identify peaks easier. Then, step 1408comprises changing the combinations of wavelengths examined in theimage. Then, step 1410 comprises removing pixels where the variationexceeds a threshold. Steps 1401, 1406, 1408 and 1410 may be repeateduntil an desired condition is found as indicated by the decision blockof step 1412. When desired conditions are found, then step 1414comprising running a calibration substrate to connect peak shifts tothickness shifts may be performed. As mentioned above, it will berecognized that in some embodiments only a subset of the optimizationsteps of step 1404, step 1406, step 1408, and step 1410 may be utilized.Furthermore, the order shown for step 1404, step 1406, step 1408, andstep 1410 is merely exemplary, and the order of the steps may berearranged. In this manner, one or more techniques for improving theimage analysis of a spinning substrate while that substrate is beingcoated with a film may be utilized.

Process Control Techniques for Film Thickness Control Utilizing FluidDispense System Camera and Other Process Variables for Detecting ProcessVariations Including Viscosity Changes

The camera image data collected herein may be combined with a widevariety of other data so as to better monitor, characterize and/orcontrol a substrate processing process flow. In one example, the cameraimage data may be combined with data collected from a WIS. In anotherembodiment, the camera image data may be combined with other datacollected from the other fluid dispense system components. Stillfurther, the image data may be combined with other data such as datarelated to the source of the liquid being dispensed (which liquid sourcebottle, the liquid source bottle age, etc.).

For example, the techniques described above may be utilized to determinefilm thickness in a fluid dispense system. Further, as mentioned, thetechniques may be utilized to identify variability between differentsubstrates processed fluid dispense systems. In addition, the techniquesdescribed may be utilized to provide real-time control of a fluiddispense system so as to obtain a desired film thickness. A wide rangeof variations in process variables may cause the changes seen in filmthickness. These variations may include variations in the incomingsubstrates to be processed, variations in the performance of the processequipment (inaccuracies/changes in spin speed, temperature, dispensevolume, etc.), and variations in the material being dispensed. Onevariable related to the material being dispensed is viscosity. Materialviscosity may change for a number of reasons. For example, a supply tankor bottle may be the source of the liquid material supplied to the fluiddispense system. When a new bottle is utilized as a source, theviscosity may vary from the prior bottle. Further, the viscosity fromthe source may change over time.

To better provide better process control to the overall substrateprocess flow to account for process variations discussed, the cameraimage data obtained from the fluid dispense system may be combined withother data obtained from the substrate and/or process equipment duringthe process flow. For example, as mentioned above, a substrate may moveto a WIS after coating the material in the fluid dispense system. Inmany cases the WIS typically is utilized in the substrate process flowafter the substrate has been coated and subjected to a PAB unit. Acamera configuration similar to that described with regard to the fluiddispense system 60 may be utilized to provide an image for coloranalysis of the substrate in the WIS. The color analysis may provideinformation as to the thickness of the coating. Such information may notinclude the time variation of the spin drying process described aboveand may be more susceptible to underlying substrate variability.However, the color image data obtained in the WIS does account for theoverall coat and bake process.

By combining the image data obtained from the fluid dispense system withdata subsequently obtained in a WIS, more detailed information may beprovided for determining film thickness, providing process controland/or determining if an issue which should be flagged has occurred inthe process flow.

Moreover, other data collected from the process flow may also becombined with the WIS image data and the fluid dispense system imagedata. For example, various parameters may be obtained from sensors inthe fluid dispense system (spin motor data, temperature, dispense time,etc.). In addition, data as to the status of the material beingdeposited may be obtained (bottle change, age of bottle, etc.). All suchinformation may then be combined (or a sub-set of the information) so asto provide better process control, characterization and monitoring ofthe film formation process flow. One exemplary process controlconfiguration is shown in FIG. 15. As shown in FIG. 15, data may becollected from non-camera sensors of the fluid dispense system as shownby block 1502. Fluid dispense system image data from the camera may becollected as shown by block 1504. WIS image data may be collected asshown by block 1506. Material status data may be collected as shown byblock 1508. As shown in the figure, all of the collected data may beprovided to the controller 94 (such as the controller 94 shown in FIG.1). The controller 94 may then provide an output 1510 based uponanalysis of all the data provided. The output 1510 may be utilized toprovide control signals to some aspect of the process flow (for exampleadjustments to various process variables), may be utilized to flag theexistence of some process deviation or fault, may be merely collectedfor future analysis/characterization of the process, etc. In thismanner, a more complex analysis of the film formation process may occurutilizing the image data of the fluid dispense system as one piece ofthe data being analyzed. It will be recognized that all of the dataillustrated in FIG. 15 need not be utilized, but rather a sub-set ofdata may be utilized. For example, in another embodiment, only data fromthe fluid dispense system sensors, the fluid dispense system image dataand the material status data may be utilized. It will be recognized thatother combinations of data with the fluid dispense system image data mayalso be utilized, including combinations with data sources not shown.

It will be recognized that the substrates described herein may be anysubstrate for which the substrate processing is desirable. For example,in one embodiment, the substrate may be a semiconductor substrate havingone or more semiconductor processing layers (all of which together maycomprise the substrate) formed thereon. Thus, in one embodiment, thesubstrate may be a semiconductor substrate that has been subjected tomultiple semiconductor processing steps which yield a wide variety ofstructures and layers, all of which are known in the substrateprocessing art, and which may be considered to be part of the substrate.For example, in one embodiment, the substrate may be a semiconductorwafer having one or more semiconductor processing layers formed thereon.Although the concepts disclosed herein may be utilized at any stage ofthe substrate process flow, the monitoring techniques described hereinmay generally be performed before, during or after a substrate issubject to a fluid dispense operation.

FIGS. 16-21 illustrate exemplary methods for use of the processingtechniques described herein. It will be recognized that the embodimentsof FIGS. 16-21 are merely exemplary and additional methods may utilizethe techniques described herein. Further, additional processing stepsmay be added to the methods shown in the FIGS. 16-21 as the stepsdescribed are not intended to be exclusive. Moreover, the order of thesteps is not limited to the order shown in the figures as differentorders may occur and/or various steps may be performed in combination orat the same time.

FIG. 16 illustrates an exemplary method of monitoring one or morecharacteristics of a fluid dispense system. The method includes step1605 of providing a substrate within the fluid dispense system. Themethod also includes step 1610 of obtaining a camera image of thesubstrate within the fluid dispense system. The method also includesstep 1615 of determining a location of at least one edge of thesubstrate from the camera image. The method also includes step 1620 ofutilizing information regarding the location of at least one edge of thesubstrate to determine a placement of the substrate within the fluiddispense system.

FIG. 17 illustrates an exemplary method of monitoring one or morecharacteristics of a fluid dispense system. The method includes step1705 of providing a substrate within the fluid dispense system. Themethod also includes step 1710 of forming a liquid puddle on thesubstrate. The method also includes step 1715 of obtaining a cameraimage of the puddle formed on the substrate. The method also includesstep 1720 of identifying edges of the puddle from the camera image ofthe puddle.

FIG. 18 illustrates an exemplary method of monitoring one or morecharacteristics of a fluid dispense system. The method comprises step1805 of providing a cup within the fluid dispense system. The methodalso includes step 1810 of obtaining a camera image of the cup withinthe fluid dispense system. The method further includes step 1815 ofdetermining a location of at least one edge of the cup from the cameraimage. The method also includes step 1820 of utilizing informationregarding the location of at least one edge of the cup to analyze aplacement of the cup within the fluid dispense system.

FIG. 19 illustrates an exemplary method of monitoring one or morecharacteristics of a fluid dispense system. The method comprises step1905 of providing a substrate within the fluid dispense system. Themethod also includes step 1910 of spin coating a material upon thesubstrate. The method further includes step 1915 of utilizing a camerato obtain image data of the substrate over time while the material isbeing spin coated on the substrate. The method also includes step 1920of obtaining reflected intensity data over time from the image data. Themethod further includes step 1925 of utilizing the reflected intensitydata over time to monitor and/or characterize the spin coating of thematerial upon the substrate.

FIG. 20 illustrates an exemplary method of monitoring one or morecharacteristics of a fluid dispense system. The method comprises step2005 of providing a substrate within the fluid dispense system. Themethod also comprises step 2010 of spin coating a material upon thesubstrate. The method further comprises step 2015 of utilizing a camerato obtain image data of the substrate over time while the material isbeing spin coated on the substrate. The method also includes step 2020of obtaining reflected intensity data over time from the image data. Themethod further includes step 2025 of utilizing signal processingtechniques on the reflected intensity data to account for movementwithin the fluid dispense system.

FIG. 21 illustrates an exemplary method of monitoring, characterizing orcontrolling a substrate process flow. The method comprises step 2105 ofproviding a substrate within a fluid dispense system. The method alsoincludes step 2110 of obtaining a camera image of the substrate withinthe fluid dispense system, the camera image being a still image or avideo image. The method further includes step 2115 of collecting imagedata from the camera image. The method also includes step 2120 ofcombining the image data with other data related to the substrateprocess flow so as to monitor, characterize or control the substrateprocess flow.

Further modifications and alternative embodiments of the inventions willbe apparent to those skilled in the art in view of this description.Accordingly, this description is to be construed as illustrative onlyand is for the purpose of teaching those skilled in the art the mannerof carrying out the inventions. It is to be understood that the formsand method of the inventions herein shown and described are to be takenas presently preferred embodiments. Equivalent techniques may besubstituted for those illustrated and described herein and certainfeatures of the inventions may be utilized independently of the use ofother features, all as would be apparent to one skilled in the art afterhaving the benefit of this description of the inventions.

What is claimed is:
 1. A method of monitoring one or morecharacteristics of a fluid dispense system, the method comprising:providing a substrate within the fluid dispense system; forming a liquidpuddle on the substrate; obtaining a camera image of the puddle formedon the substrate; and identifying edges of the puddle from the cameraimage of the puddle.
 2. The method of claim 1, wherein identification ofthe edges of the puddle is utilized to determine a percentage of thesubstrate that is covered by the puddle.
 3. The method of claim 1,wherein identification of the edges of the puddle is utilized toidentify non-idealities in a shape of the puddle.
 4. The method of claim1, wherein the camera image of the puddle is obtained before thesubstrate spins.
 5. The method of claim 1, further comprising obtaininga plurality of camera images of the puddle formed on the substrate. 6.The method of claim 5, wherein at least one of the plurality of cameraimages of the puddle is obtained while the substrate is spinning.
 7. Themethod of claim 1, wherein the edges of the puddle are determined basedon an intensity analysis of the camera image.
 8. The method of claim 7,wherein the intensity analysis is performed on a subset of pixels of thecamera image.
 9. The method of claim 7, wherein a number of pixels thatcorrespond to liquid coverage is determined.
 10. The method of claim 9,wherein the intensity analysis is performed on a plurality of cameraimages.
 11. The method of claim 1, further comprising: providing a lightsource; providing a camera, the camera receiving light from the lightsource that is reflected off the substrate; and coupling a controller tothe camera, the controller configured to receive data from the cameraregarding light reflected from the substrate when one or more fluids aredispensed on the substrate, the controller processing the data so as toselectively consider only a subset of pixels of the data from the camerato monitor a condition of a fluid dispensed on the substrate.
 12. Themethod of claim 1, wherein the condition of the fluid dispensed on thesubstrate is a condition of the liquid puddle.
 13. The method of claim11, wherein the use of only a subset of pixels of the data provides anoutput having less noise than if all available pixels are used.
 14. Themethod of claim 13, wherein the subset of pixels selected includes a0^(th) order reflection of light reflected off the substrate.
 15. Amethod of monitoring one or more characteristics of a fluid dispensesystem, the method comprising: providing a substrate within the fluiddispense system; spin coating a material upon the substrate; utilizing acamera to obtain image data of the substrate over time while thematerial is being spin coated on the substrate; obtaining reflectedintensity data over time from the image data; and utilizing thereflected intensity data over time to monitor and/or characterize thespin coating of the material upon the substrate.
 16. The method of claim15, wherein reflected intensity data over time is obtained for aplurality of substrates and the monitoring and/or characterizingincludes comparison of reflected intensity data of two or moresubstrates.
 17. The method of claim 15, wherein peaks and/or troughs ofthe reflected intensity data are utilized to monitor and/or characterizethe spin coating of the material upon the substrate.
 18. A method ofmonitoring one or more characteristics of a fluid dispense system, themethod comprising: providing a substrate within the fluid dispensesystem; spin coating a material upon the substrate; utilizing a camerato obtain image data of the substrate over time while spin coating thematerial upon the substrate; obtaining reflected intensity data overtime from the image data; and utilizing signal processing techniques onthe reflected intensity data to account for movement within the fluiddispense system.
 19. The method of claim 18, wherein the movement withinthe fluid dispense system is movement of a pattern on the substrateresulting from the substrate spinning.
 20. The method of claim 18,wherein the signal processing techniques include smoothing of thereflected intensity data over time.
 21. The method of claim 18, whereinthe signal processing techniques include analyzing only one or moreselected wavelengths of the reflected intensity data.
 22. The method ofclaim 18, wherein the signal processing techniques include transformingthe reflected intensity data.
 23. The method of claim 18, wherein theimage data includes data from a plurality of pixels of the camera, thesignal processing techniques include analyzing only image data from asub-set of the plurality of pixels of the camera.